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Graph Name Retrieved From View
workflow graph Replace legacy AML Trio Assay

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/aml_trio_cle.cwl

Branch/Commit ID: 8438316338e66823e1c9aca9f675b2bf33f2aa59
workflow graph DESeq - differential gene expression analysis

Differential gene expression analysis ===================================== Differential gene expression analysis based on the negative binomial distribution Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. DESeq1 ------ High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. Simon Anders and Wolfgang Huber propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, [DESeq](http://bioconductor.org/packages/release/bioc/html/DESeq.html), as an R/Bioconductor package DESeq2 ------ In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. [DESeq2](http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html), a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.

https://github.com/datirium/workflows.git

Path: workflows/deseq.cwl

Branch/Commit ID: 42dc4f70b117e78785b82865ec4c4b941ac1c259
workflow graph Downsample and HaplotypeCaller

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/downsample_and_recall.cwl

Branch/Commit ID: 1750cd5cc653f058f521b6195e3bec1e7df1a086
workflow graph exome alignment and germline variant detection, with optitype for HLA typing

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/germline_exome_hla_typing.cwl

Branch/Commit ID: 8438316338e66823e1c9aca9f675b2bf33f2aa59
workflow graph umi molecular alignment workflow

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/molecular_qc.cwl

Branch/Commit ID: 788bdc99c1d5b6ee7c431c3c011eb30d385c1370
workflow graph count-lines6-wf.cwl

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/count-lines6-wf.cwl

Branch/Commit ID: bfe56f3138e9e6fc0b9b8c06447553d4cea03d59
workflow graph Detect DoCM variants

https://github.com/genome/analysis-workflows.git

Path: definitions/subworkflows/docm_germline.cwl

Branch/Commit ID: 4a04ad33e311c5e647cef848b74034477cb3c47e
workflow graph Detect Variants workflow for nonhuman WGS pipeline

https://github.com/genome/analysis-workflows.git

Path: definitions/pipelines/detect_variants_wgs_nonhuman.cwl

Branch/Commit ID: d57c2af01a3cb6016e5a264f60641eafd2e5aa05
workflow graph any-type-compat.cwl

https://github.com/common-workflow-language/cwltool.git

Path: cwltool/schemas/v1.0/v1.0/any-type-compat.cwl

Branch/Commit ID: 10492acee927c177933160f6ad67085f9112b0d1
workflow graph rnaseq-pe-dutp.cwl

Runs RNA-Seq BioWardrobe basic analysis with strand specific pair-end data file.

https://github.com/Barski-lab/workflows.git

Path: workflows/rnaseq-pe-dutp.cwl

Branch/Commit ID: bc75349ad3a7bdce82b4cd8584501f4d0280bb8d